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Estimation of Wind Drift and Evaporation Losses from Sprinkler Irrigation systemS by Different Data‐Driven Methods
Author(s) -
Maroufpoor E.,
Sanikhani H.,
Emamgholizadeh S.,
Kişi Ö.
Publication year - 2018
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.2182
Subject(s) - adaptive neuro fuzzy inference system , mean squared error , gene expression programming , pan evaporation , wind speed , artificial neural network , coefficient of determination , statistics , mathematics , evaporation , computer science , meteorology , machine learning , fuzzy logic , artificial intelligence , fuzzy control system , geography
An accurate estimation of losses created by wind drift and evaporation is necessary for water management in sprinkler irrigation systems. In the current study, three artificial intelligence (AI) methods, namely artificial neural network (ANN), adaptive neuro‐fuzzy inference system (ANFIS) and gene expression programming (GEP), were used to estimate the wind drift and evaporation losses (WDEL) based on influential variables such as operating pressure ( P ), wind speed ( W ), nozzle diameter ( D ) and vapour pressure deficit ( e s − e a ). Field measurements data were applied to training and testing of ANN, ANFIS and GEP models. The results of these AI models were also compared with previous studies. Three statistical metrics, namely coefficient of determination ( R 2 ), root mean square error (RMSE) and mean absolute error (MAE), were utilized to assess the performance of the models. The results indicate that the AI models (i.e. ANFIS, ANN and GEP) predict the WDEL more accurately than in previous studies. Moreover, the results show that the GEP model (with RMSE = 1.19% and R 2 = 0.97) provides a better estimation of WDEL compared to the ANN model (with RMSE = 2.56% and R 2 = 0.87) and ANFIS model (with RMSE = 1.64% and R 2 = 0.94). Copyright © 2017 John Wiley & Sons, Ltd.